Read across for the derivation of Indoor Air Guidance Values supported by PBTK modelling

Polyurethane Flexible Foams (PUF) are versatile materials used in upholstered furniture and bed mattresses. Due to the production procedure, fresh foams emit volatile organic compounds (VOC) which may contribute to indoor air exposure. To evaluate the risk for consumers, the VOC concentration measured in chamber tests can be matched against existing benchmarks for indoor air like “Richtwerte” (RW) of the German UBA (Umweltbundesamt), “Lowest Concentration of Interest” (LCI) for construction products or derived no effect levels (DNEL) for consumer inhalation exposure. In a previous paper a method for the derivation of Indoor Air Guidance Values (IAGV) for VOC without RW, LCI or DNEL was developed. The method described made use of a sufficient toxicological database. For substances with an insufficient database, read across to structural analogues is a way forward to estimate Indoor Air Guidance Values (IAGV). In this work a read across exercise, supported by an open source physiology based toxicokinetic (PBTK) modelling program is demonstrated. The use of enzyme kinetic data for phase I and phase II metabolism is discussed and areas for further work were identified. For two substances with very limited toxicological data, allyloxypropanol (isomer mixture of 1-allyloxy-2-propanol and 2-allyloxy-1-propanol) and 2,3-di-ethyl-2,3-dimethylsuccinodintrile, Tentative Indoor Air Guidance Values of 750 µg/m³ and 65 µg/m³ were derived.


S2
At low VOC concentrations the Michaelis-Menten kinetic is expected to be first order, as expressed by the ratio Vmax / Km, which is a first order reaction rate constant. The difference in this reaction rate for CYP metabolism (Vmax/Km), according to Table 1, is up to 168. For the read across between substances based on the PBTK model IndusChemFate v2.1, the median was used for Vmax and Km (Table A2). For read across purposes it is generally accepted that the toxic action of a compound is driven by its concentration over time in the target organ -the toxicokinetic factor -and its specific interaction with the target tissue -the toxicodynamic factor. The toxicokinetic factor can be addressed by PBTK modelling. If specific data for a substance are missing it is always critical what is the right assumption for a metabolic turnover. In this work, it is assumed that the target compound and its reference compound are not different against CYP 450 mediated reactions. However, to demonstrate potential critical issues it is assumed that the target compound may show a CYP 450 mediated turnover at the third quartile and the reference compound having an activity at the first quartile, or vice versa, whatever would represent the worst case.

Propenyloxypropanol (Allyloxypropanol, AOP)
Table A3 below summarizes the physical properties of allyloxypropanols against their read across candidate 2,2-Bis(allyloxy)-2-ethyl-butanol-1-(BAB). These physical data were used to run a PBPK modelling with the free software IndusChemFate v2.0 (Jongeneelen and ten Berge, 2011). The concentration in air was set to 1 mg/m³, and 28 day non-stop exposure of a man of 70 kg body weight was modelled. In absence of data, enterohepatic circulation was set to zero and renal re-absorption was set to "unknown". First, concentrations of the respective parent compounds were calculated, assuming an equivalent turnover by phase I metabolism. The assumed CYP 450 activities are given in Table A2.  If for both compounds, BAB and AOP, the same metabolic turnover in liver is assumed, nearly identical steady state concentrations will be achieved in this organ ( Figure A1). However, if BAB is degraded by a rate equivalent to the 3 rd quartile of the CYP activity, but AOP is transformed at a 1 st quartile rate, only, a five fold smaller air concentration of AOP is required to achieve equal steady state levels in human liver ( Figure A2). The phase I metabolism may create epoxides, which can add to biological macromolecules and, therefore, epoxidation is a toxicification. Phase II metabolism may detoxify the epoxides, namely due to hydrolysis catalyzed by epoxyhydrolase (EH) and due to addition of glutathione (GSH), mediated by glutathione-S-transferase (GST).

1-(Allyloxy)-2propanol
Enzymatic parameters for these phase II metabolism steps are published by Csanady et al. (1994Csanady et al. ( , 2003 and are summarized in Table A4. The initial concentrations of GSH in liver and lung are 5.9 and 2.0 mM, respectively, and the zero order production rates of GSH in liver and lung are 0.9 and 0.3 mM/h, respectively.  Table A4: Enzyme parameters for human lung and liver epoxyhydrolase and glutathione-Stransferase (Csanady et al., 1994(Csanady et al., , 2003.

Vmax [M/h] Km,S [M] Km,GSH [M]
GST lung 8.2E-02 2.5E-03 1.0E-04 GST liver 2.8E-02 2.5E-03 1.0E-04 EH lung 6.7E-04 1.8E-05 ----EH liver 4.5E-03 1.0E-05 ---- Epoxide hydrolysis and addition of GSH to epoxides are parallel reactions, both mitigating the epoxide. While the action of epoxide hydrolase can be modelled by simple Michaelis-Menten kinetics, the reaction with glutathione is a bi-substrate reaction with a more complicated rate expression. The decay rate of epoxide is the sum of both reaction rates and can be expressed as , , , , , , , (Csanady et al., 1994(Csanady et al., , 2003. The first term on the right hand side describes the epoxide decay catalyzed by epoxide hydrolase, whereas the second term describes the epoxide reaction with glutathione. This expression is too complicated to introduce it into the convenient In-dusChemFate program. However, a few approximations can be introduced. One approximation is the assumption, that GST and GSH don't play a role (worst case), and only epoxide hydrolase serves for the decay of epoxide. The other assumption is that the epoxide concentration is always that low, that the concentration of GSH is not changed and maintained at 2.0 mM in the lung and 5.9 mM in the liver (Csanady et al., 2003). At such conditions it can be assumed that Km is much larger than the substrate concentration, and the following simplifications are introduced: As the second term contains [S]² while [S] is assumed to be very small, the second term is expected to be much smaller than the first term and, therefore, omitted: Now, the following new constants are introduced, At this point, it is assumed that phase I metabolism is a toxification reaction, whereas phase II metabolism is a detoxification. The steady state concentration of the epoxide following continuous air exposure shall be modelled. Table A5 lists the calculated physico-chemical parameters for the allyloxypropanol (AOP) oxidation product, the Glycidyl-(2hydroxy)propyl ether (AOP epoxide), and for the 2,2-Bis(Allyloxymethyl)-2-ethyl-butanol-1 (BAB) epoxidation product, the 2-Allyloxymethyl-2-glycidyloxymethyl-2ethyl-butanol-1 (BAB epoxide). As worst case it is assumed that epoxidation of BAB is slow (1 st quartile for CYP activity in liver) and AOP epoxidation is fast (3 rd quartile CYP acitivity in liver).  For the AOP epoxide and BAB epoxide, Vmax # and Km # for liver and lung are introduced in the model, and calculations are run under assumption of constant GSH concentrations. However, the level of GSH may become exhausted, which would finally end up in some cell damage and would not be in concordance with a NOAEL. However, as this exercise is a read S6 across from BAB to AOP, the model is run also under assumption that only epoxide hydrolase serves for the decay of epoxides. Results are shown in Table A6. Naturally, the slower phase I metabolism of BAB compared to AOP results in about five fold higher levels in the liver; decay of the epoxides by EH alone or by EH and GST in combination leads to nearly equivalent levels of epoxides of AOP and BAB, respectively. As can be seen in Figure A3, also for the epoxide metabolites BAB takes some more time to achieve the stady state in the liver. In case of GST and EH activity, steady state levels of epoxides in the liver are a factor of about 10 lower when compared to only EH activity. The higher epoxidation rate of AOP against BAB on the one hand causes a five fold lower concentration of the parent compound in the liver, on the other hand a higher concentration of the epoxide in the liver. However, AOP epoxide levels are only 5 to 7 % higher than BAB epoxide levels. Figure A3: Concentration (µmol/L) of BAB epoxide (squares) and AOP epoxide (circles) depending on EH activity alone (filled symbols) and combined EH and GST activity (empty symbols)
Both succinodinitriles were compared on the basis of physiology based toxicokinetic modelling. With these data, the blood concentration of the substances in man for continuous exposure against 1 mg/m³ was calculated with the program IndusChemFate v2.0 (Jongeneelen and ten Berge, 2011). Data are shown in Table A7. Based on the calculated results, the toxicokinetic difference between TMSD and DEDMSD is far less than a factor of 2 in typical VOC target organs brain, liver and kidneys.
Again, it may be assumed that the CYP 450 metabolism of TMSD is fast (3 rd quartile, Table A2) and that of DEDMSD is slow (1 st quartile, Table A2). Figure A4 shows the concentration in the target organ liver in dependence on exposure time; Figure A5 shows the concentration-time curve for the target organ kidney.   Therefore, if the turnover of TMSD in liver is high (3 rd quartile) and that of DEDMSD is low (1 st quartile), an air concentration of 0.18 mg/m³ DEDMSD would result in an equivalent steady state liver and kidney concentrations as 1 mg/m³ TMSD.